Instrument mass fractionation correction method for secondary ion mass spectrometer based on multiple regression

By correcting the instrument mass fractionation effect of the secondary ion mass spectrometer using a multiple regression method, the problem of analytical accuracy caused by fluctuations in ion beam intensity and secondary ion signal intensity was solved, thus achieving high-precision isotope analysis.

CN122364632APending Publication Date: 2026-07-10INST OF GEOLOGY CHINESE ACAD OF GEOLOGICAL SCI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
INST OF GEOLOGY CHINESE ACAD OF GEOLOGICAL SCI
Filing Date
2026-04-15
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing secondary ion mass spectrometers suffer from significant instrument mass fractionation effects during analysis, especially the nonlinear fractionation effects caused by fluctuations in ion beam intensity and secondary ion signal intensity, which limit analytical accuracy and result in low data utilization.

Method used

A multiple regression method was adopted, and a multiple linear regression model was established by simultaneously collecting isotope ratios, analysis time, primary ion beam intensity and secondary ion signal intensity data. The model included time drift, beam effect and secondary ion yield effect. The model parameters were solved by iterative reweighted least squares method and then corrected.

Benefits of technology

It effectively eliminates the additional fractionation error caused by minor fluctuations in instrument status, improves the external accuracy of isotope analysis from 0.5‰ to 0.2‰, adapts to long-term large-scale analysis, and improves the explanatory power and resistance to outliers of the model.

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Abstract

This invention discloses a method for mass fractionation calibration of a secondary ion mass spectrometer based on multiple regression. The method includes alternating measurements of standard samples and unknown samples in an analytical sequence. At each analytical point, isotope ratio measurements, analysis time, primary ion beam intensity, and secondary ion signal intensity are simultaneously acquired to obtain measurement data. The preprocessing includes calculating the single-point instrument measurement deviation of the standard sample, performing outlier removal, and variable centering. The measurement data is preprocessed to establish a multiple linear regression model incorporating time drift, beam effect, and secondary ion yield effect. The iterative reweighted least squares method is used to solve the multiple linear regression model based on the measurement data to obtain optimal model parameters. The optimal model parameters are then used to calibrate the unknown samples one by one, outputting the mass fractionation calibration results.
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